基于心电图的人类活动特定的心脏模式检测,使用机器学习和深度学习模型
Kusum Tara1, Md Hasibul Islam2, Takenao Sugi2
1Department of Biological and Material Engineering, Graduate School of Science and Engineering, Saga University, Japan.
Journal of electrocardiology
|February 21, 2025
概括
这项研究表明,使用双光谱轮的深度学习模型在各种活动中准确监测心脏模式. 这种先进的技术为各种现实场景提供可靠的心脏应激检测.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 心脏监测对于检测压力和异常至关重要.
- 在各种活动中分析心电图 (ECG) 信号模拟现实世界的压力因素.
- 心脏行为的非线性动态是心脏健康的关键指标.
研究的目的:
- 开发和比较机器学习模型来分类压力下的心脏模式.
- 评估深度学习和基于特征的方法对ECG分析的有效性.
- 识别显著的心脏特征,表明对压力因素的自主反应.
主要方法:
- 在休息,认知任务和体育活动期间收集了心电图信号.
- 一个基于特征的随机森林 (RF) 模型使用了时间,频率和统计特征.
- 一个基于图像的卷积神经网络 (CNN) 模型使用了连续波形变形 (CWT) 刻度图和基于双光谱的轮.
主要成果:
- 射频模型实现了96.80%的精度和92.22%的F1分数.
- 使用CWT扫描图的CNN模型达到98.44%的准确率和96.11%的F1分数.
- 基于双光谱轮的CNN模型获得了最高的精度 (99.16%) 和F1得分 (97.89%).
结论:
- 采用基于双光谱的轮的CNN模型在分类心脏模式方面表现出卓越的性能.
- 这种深度学习方法显示出在各种活动中可靠监测心脏功能的巨大潜力.
- 压力指数和SNS与PNS比率等关键特征有效地突出了对认知和身体压力因素的自主反应.
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An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
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An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
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